7 papers
LiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Tasks
Xiang Long, Li Du, Yilong Xu +11
OpenClaw-style personal assistants extend LLM agents from isolated tool use to open-ended, stateful, and personalized software environments. Evaluating these assistants is fundamen…
TInR: Exploring Tool-Internalized Reasoning in Large Language Models
Qiancheng Xu, Yongqi Li, Fan Liu +3
Tool-Integrated Reasoning (TIR) has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools during reasoning. Existing TIR meth…
RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation
Rui Min, Liang Yao, Shiyu Miao +5
A robust Multimodal Large Language Model (MLLM) for Earth Observation should maintain consistent interpretation and reasoning under realistic input variations. However, current Rem…
MeKi: Memory-based Expert Knowledge Injection for Efficient LLM Scaling
Ning Ding, Fangcheng Liu, Kyungrae Kim +4
Scaling Large Language Models (LLMs) typically relies on increasing the number of parameters or test-time computations to boost performance. However, these strategies are impractic…
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
Yehui Tang, Xiaosong Li, Fangcheng Liu +19
The surgence of Mixture of Experts (MoE) in Large Language Models promises a small price of execution cost for a much larger model parameter count and learning capacity, because on…
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
Yehui Tang, Yichun Yin, Yaoyuan Wang +71
Sparse large language models (LLMs) with Mixture of Experts (MoE) and close to a trillion parameters are dominating the realm of most capable language models. However, the massive…